When a mobile terminal makes radio communications with a plurality of base stations, a signal intensity of radio waves received by the mobile terminal attenuates depending on a distance of the mobile terminal from each of the base stations, and a position estimating technique has been proposed to estimate a location of the mobile terminal using the signal intensity attenuation. The base station may be an AP (Access Point) using Wi-Fi (Wireless-Fidelity, registered trademark).
According to this proposed position estimating technique, the mobile terminal collects an ID (Identifier) of the base station and an RSSI (Received Signal Strength Indicator) received beforehand at each location. An RSSI feature vector that is uniquely determined for each location is created from numerical values of the IDs of the plurality of base stations and the RSSIs received at each location, so as to create a location model for each location. The location model is a database indicating the RSSIs of the radio waves received from each of the base stations at each of the locations. When estimating the location, the RSSIs of the radio waves the mobile terminal receives from the base stations are collated with the location model, in order to estimate the location of the mobile terminal. In general, the location model may be created according to methods such as the k-NN (k-Nearest Neighbor algorithm), the probability method based on a probability distribution, the non-parametric method, and the pattern matching method which will be described hereinafter.
The k-NN first stores pairs of RSSI feature vectors and location names collected at each location. Next, when estimating the location, a newly observed RSSI feature vector and samples within the database are collated, in order to extract K nearest neighbor samples. Finally the K nearest neighbor samples are voted to determine the location.
However, according to the k-NN, the number of samples is large, and a database having a large capacity is required to store the samples. It is difficult to store such a large-capacity database in the mobile terminal. In addition, because of the large number of samples, it takes a long time to perform the process of collating the newly observed RSSI feature vector and the samples within the database. Furthermore, in a case in which the RSSIs change due to a AP that is newly set up, the AP that is removed, the AP that is moves, changes in a layout of furniture within the location, or the like, it is difficult to perform an on-line updating of the database. Hence, with respect to such a change in the RSSIs, measures such as discarding the database that has been configured and configuring a new database, for example, are required.
The probability method based on the probability distribution include the parametric method and the non-parametric method.
The parametric method computes model parameters by fitting samples of the RSSI feature vectors collected at one location for various orientations at which the mobile terminal is held, to a statistical model. For example, an average and a covariance of a sample distribution for each location become the model parameters of this type of location model. When estimating the location of the mobile terminal, the newly observed RSSI feature vector is fitted to an RSSI distribution at each location, in order to compute an observation probability for a case in which the observation is made at the location. The location where the observation probability becomes a maximum is output as an estimated location.
However, when estimating the location according to the parametric method, the observation probability of the newly observed RSSI feature vector must be computed for all location models. For this reason, in a case in which the number of candidate locations is large, it takes a long time to perform the computing process. In addition, similarly as in the case of the k-NN, it is difficult to perform on-line updating of the location model. In addition, because the location estimation is a relative evaluation of the probability (that is, the location having the maximum probability is output), it is impossible to cope with a case in which the mobile terminal is at a location other than a learned location.
The non-parametric method utilizes an observation frequency histogram of the intensity levels of the RSSIs, instead of utilizing the average and the covariance of the parameters of the location model. In general, the value of the RSSI changes because the radio waves are reflected or blocked by surrounding walls, obstructions, or the like. For this reason, it is difficult to accurately represent the RSSI distribution by the parameters of the location model, such as the simple average and the covariance. Hence, a more accurate location model can be generated by utilizing the observation frequency histogram of the intensity levels of the RSSIs as the probability distribution. More particularly, when estimating the location of the mobile terminal, the observation probability of the newly observed RSSI feature vector is computed based on an RSSI intensity histogram, and the location where the observation probability becomes a maximum is output as the estimated location.
However, when estimating the location according to the non-parametric method, the observation probability of the newly observed RSSI feature vector must be computed for all location models. For this reason, in a case in which the number of candidate locations is large, it takes a long time to perform the computing process. In addition, it is difficult to perform the on-line updating of the location model. Further, because the location estimation is a relative evaluation of the probability (that is, the location having the maximum probability is output), it is impossible to cope with a case in which the mobile terminal is at a location other than the learned location.
The pattern matching method performs a more accurate modeling of the RSSI distribution, by utilizing a location model that is more complex compared to that utilized by the probability method described above. The pattern matching method includes the ANN (Artificial Neural Network), the SVM (Support Vector Machine), the GP (Gaussian Process), or the like, for example.
However, according to the pattern matching method, the observed RSSI feature vector must be collated with a large number of location models, thereby taking a long time to perform the computing process and increasing the computation cost. In addition, it is difficult to perform the on-line updating of the location model. Further, relearning of the location model is required in a case in which the AP is newly set up, the AP is removed, the AP is moved, the layout of the furniture within the location changes, or the like.
Related art include Japanese Laid-Open Patent Publications No. 2010-239331 and No. 2011-179946, Teemu Roos et al., “A Probabilistic Approach to WLAN User Location Estimation”, International Journal of Wireless Information Networks, Vol. 9, No. 3, July 2002, and Ville Honkavirta et al., “A Comparative Survey of WLAN Location Fingerprinting Methods”, Proc. of the 6th Workshop on Positioning, Navigation and Communication 2009 (WPNC '09), Pages 243-251, for example.
According to the conventional position estimating methods, it takes a long time to perform the process and a high-speed estimation of the location is difficult, in a case in which the location of the mobile terminal is to be estimated from a large number of candidate locations on the order of several ten-thousand or more, for example.